{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer 4.0 Alpha with parametrized curve"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/fsrs4anki_optimizer_alpha_parametrized-curve.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n"
     ]
    },
    {
     "data": {
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       "model_id": "23604cf0826f4d0388a44807c78f3eb3",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "0431811c0e43410a8ef28e3678ca2f13",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "9ea4f2cb7f2c4d199865486f1f8d9734",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.876        4.2    4.20       4176\n",
      "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
      "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
      "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
      "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
      "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6589\n",
      "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
      "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
      "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
      "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
      "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
      "3,3,3,3,3,3,3,3         131.1          0.970      617.2    3.97        100\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       38.9    3.21       7517\n",
      "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
      "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
      "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
      "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
      "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
      "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "df['i'] = 1\n",
    "df['r_history'] = \"\"\n",
    "df['t_history'] = \"\"\n",
    "col_idx = {key: i for i, key in enumerate(df.columns)}\n",
    "\n",
    "\n",
    "# code from https://github.com/L-M-Sherlock/anki_revlog_analysis/blob/main/revlog_analysis.py\n",
    "def get_feature(x):\n",
    "    last_kind = None\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if last_kind is not None and last_kind in (1, 2) and log.type == 0:\n",
    "            return x.iloc[:idx]\n",
    "        last_kind = log.type\n",
    "        if idx == 0:\n",
    "            if log.type != 0:\n",
    "                return x.iloc[:idx]\n",
    "            x.iloc[idx, col_idx['delta_t']] = 0\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, col_idx['i']] = x.iloc[idx, col_idx['i']] + 1\n",
    "        x.iloc[idx + 1, col_idx['t_history']] = f\"{x.iloc[idx, col_idx['t_history']]},{x.iloc[idx, col_idx['delta_t']]}\"\n",
    "        x.iloc[idx + 1, col_idx['r_history']] = f\"{x.iloc[idx, col_idx['r_history']]},{x.iloc[idx, col_idx['r']]}\"\n",
    "    return x\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(get_feature)\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "def cal_retention(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group['retention'] = round(group['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x]).mean(), 4)\n",
    "    group['total_cnt'] = group.shape[0]\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history', 'delta_t'], group_keys=False).progress_apply(cal_retention)\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 3, 1, 1, 0.2, 6, 0.12, 1, 2, 0.2, 0.2, 1, 1, 1, 2, 3, 4, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "def next_interval(retention, staiblity, f):\n",
    "    return max(1, round(np.power(np.log(retention)/np.log(0.9), 1/f) * staiblity))\n",
    "\n",
    "def forgetting_curve(s, t, f):\n",
    "    return np.power(0.9, np.power(t / s, f))\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + self.w[6] *\n",
    "                        (10 * torch.pow(new_d, -self.w[13])) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * \\\n",
    "                        self.post_lapse_stability_bonus(state[:,0]) * \\\n",
    "                        torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def post_lapse_stability_bonus(self, s):\n",
    "        offset = torch.pow(1/self.w[11],1/(self.w[11]-1))\n",
    "        return torch.pow(s+offset, self.w[11]) - torch.pow(offset, self.w[11])\n",
    "    \n",
    "    def exp_forgetting_curve(self, s, t):\n",
    "        return torch.pow(0.9, torch.pow((t / s).clamp(0.001, 1000), self.w[18]))\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        keys = torch.tensor([1, 2, 3, 4])\n",
    "        keys = keys.view(1, -1).expand(X[:,1].long().size(0), -1)\n",
    "        index = (X[:,1].long().unsqueeze(1) == keys).nonzero(as_tuple=True)\n",
    "        X_rating = X[:,1].clone().float()\n",
    "        X_rating[index[0]] = self.w[14+index[1]]\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X_rating - self.w[14])\n",
    "            new_d = self.w[2] - self.w[3] * (X_rating - self.w[16])\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "        else:\n",
    "            r = self.exp_forgetting_curve(state[:,0], X[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X_rating - self.w[16])\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 15)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0, 0.5)\n",
    "            w[6] = w[6].clamp(0, 10)\n",
    "            w[7] = w[7].clamp(0.01, 0.5)\n",
    "            w[8] = w[8].clamp(0.01, 2)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.1, 2)\n",
    "            w[14] = w[14].clamp(0.1, 10)\n",
    "            w[15] = w[15].clamp(0.1, 10)\n",
    "            w[16] = w[16].clamp(0.1, 10)\n",
    "            w[17] = w[17].clamp(0.1, 10)\n",
    "            w[18] = w[18].clamp(0.1, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0 and remainder > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        elif full_batches > 0 and remainder == 0:\n",
    "            self.batch_indices = np.split(indices, full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.pre_train_data_loader.batch_sampler.batch_nums + self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "\n",
    "        epoch_len = len(self.train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "\n",
    "            for name, param in self.model.named_parameters():\n",
    "                print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "            \n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "                \n",
    "            for name, param in self.model.named_parameters():\n",
    "                print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(self.train_set))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = self.model.exp_forgetting_curve(stabilities, delta_ts)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(self.test_set))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d92975d26d94cd9baa7a54330a118c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 178072 TEST: 45723\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd83f3a3973c4a1f9c8665d4f1f1c9ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/890360 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3386\n",
      "Loss in testset: 0.2952\n",
      "w: [0.8497, 1.4968, 3.0, 1.0, 1.0, 0.2, 6.0, 0.12, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.5197, 1.8104, 3.5116, 4.0, 0.5965]\n",
      "w: [0.797, 1.6806, 2.1529, 0.8165, 1.2277, 0.0522, 6.1137, 0.1495, 1.198, 1.7624, 0.404, 0.6651, 1.1176, 1.3574, 0.1195, 2.0555, 3.485, 5.1275, 0.5231]\n",
      "Loss in trainset: 0.3197\n",
      "Loss in testset: 0.2752\n",
      "w: [0.3554, 2.0245, 2.0612, 0.7381, 1.2117, 0.0455, 6.1422, 0.1212, 1.2241, 1.7996, 0.3697, 0.7176, 1.1633, 1.3513, 0.1, 2.0272, 3.591, 5.1446, 0.5987]\n",
      "w: [0.4454, 2.2622, 2.1408, 0.8266, 1.4021, 0.1304, 6.2311, 0.2145, 1.3741, 1.6476, 0.3745, 0.8085, 1.3399, 1.364, 0.1642, 2.1525, 3.9357, 5.0999, 0.6099]\n",
      "Loss in trainset: 0.3212\n",
      "Loss in testset: 0.2756\n",
      "w: [0.4073, 2.3396, 2.1406, 0.8233, 1.4672, 0.1028, 6.255, 0.1989, 1.397, 1.6073, 0.4067, 0.7704, 1.3419, 1.3727, 0.1108, 2.0667, 3.9783, 5.1239, 0.4263]\n",
      "w: [0.4851, 2.4053, 2.118, 0.6789, 1.402, 0.1171, 6.2649, 0.2151, 1.4213, 1.5347, 0.4076, 0.7422, 1.3878, 1.4719, 0.2172, 2.3437, 3.7688, 5.4449, 0.5049]\n",
      "Loss in trainset: 0.3193\n",
      "Loss in testset: 0.2742\n",
      "w: [0.3867, 2.4338, 2.1175, 0.6737, 1.4236, 0.095, 6.2595, 0.2155, 1.4163, 1.5217, 0.4186, 0.7305, 1.3807, 1.4828, 0.2227, 2.2994, 3.7602, 5.4848, 0.4436]\n",
      "w: [0.3827, 2.4369, 2.0719, 0.6261, 1.2797, 0.1043, 6.2745, 0.225, 1.4304, 1.5225, 0.3753, 0.6893, 1.42, 1.533, 0.1279, 2.4934, 3.8164, 5.3303, 0.5547]\n",
      "Loss in trainset: 0.3187\n",
      "Loss in testset: 0.2735\n",
      "w: [0.3903, 2.4395, 2.0544, 0.6048, 1.2661, 0.1123, 6.2881, 0.2223, 1.4433, 1.5331, 0.3667, 0.6997, 1.428, 1.5218, 0.1465, 2.4955, 3.8042, 5.3357, 0.507]\n",
      "w: [0.3873, 2.4446, 2.0741, 0.613, 1.2635, 0.0968, 6.266, 0.2393, 1.422, 1.5254, 0.3713, 0.6965, 1.4153, 1.5528, 0.1219, 2.4891, 3.8343, 5.3031, 0.5215]\n",
      "Loss in trainset: 0.3186\n",
      "Loss in testset: 0.2738\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 181664 TEST: 42131\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db3c4903c0b645dc954c4e20aade3ec6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/908320 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3184\n",
      "Loss in testset: 0.3788\n",
      "w: [1.6635, 1.8196, 3.0, 1.0, 1.0, 0.2, 6.0, 0.12, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.2685, 1.762, 3.4003, 5.1442, 0.7403]\n",
      "w: [1.7352, 1.9378, 2.8028, 1.1639, 1.4444, 0.0168, 5.938, 0.0365, 1.0327, 1.7344, 0.4648, 0.6815, 1.4903, 1.2334, 0.1, 1.5027, 3.8756, 5.363, 0.4236]\n",
      "Loss in trainset: 0.3003\n",
      "Loss in testset: 0.3660\n",
      "w: [1.8284, 2.2096, 2.8127, 1.237, 1.4247, 0.0, 5.8799, 0.0352, 0.9772, 1.6707, 0.5293, 0.6668, 1.4244, 1.3011, 0.1013, 1.3814, 3.8517, 5.6542, 0.6276]\n",
      "w: [1.8724, 2.3341, 3.0182, 1.1157, 1.8056, 0.104, 5.8853, 0.01, 1.0643, 1.6599, 0.471, 0.7649, 1.8983, 1.1936, 0.1, 1.358, 4.1201, 5.8393, 0.504]\n",
      "Loss in trainset: 0.3004\n",
      "Loss in testset: 0.3669\n",
      "w: [1.9372, 2.5475, 3.0217, 1.2115, 1.8495, 0.0707, 5.868, 0.01, 1.0479, 1.6648, 0.4643, 0.7786, 1.9173, 1.2208, 0.1001, 1.2751, 4.0932, 6.0687, 0.6701]\n",
      "w: [1.9372, 2.5831, 3.2276, 1.0697, 1.7314, 0.1252, 5.7269, 0.0485, 0.9119, 1.6396, 0.4531, 0.8024, 1.9877, 1.2545, 0.1, 1.1718, 4.221, 5.9202, 0.4825]\n",
      "Loss in trainset: 0.2996\n",
      "Loss in testset: 0.3669\n",
      "w: [1.9867, 2.7278, 3.2091, 1.0612, 1.7245, 0.1446, 5.7555, 0.0219, 0.9415, 1.6702, 0.4215, 0.828, 2.0, 1.2277, 0.1, 1.1323, 4.2008, 6.0306, 0.5746]\n",
      "w: [1.9872, 2.7477, 3.2085, 0.9834, 1.7028, 0.1158, 5.7417, 0.0331, 0.9175, 1.5768, 0.4893, 0.7912, 1.9066, 1.2541, 0.1651, 1.1167, 4.2067, 6.0383, 0.5346]\n",
      "Loss in trainset: 0.2991\n",
      "Loss in testset: 0.3668\n",
      "w: [1.9843, 2.764, 3.2014, 0.9829, 1.7, 0.1173, 5.7465, 0.0262, 0.9221, 1.5826, 0.4831, 0.7943, 1.913, 1.2495, 0.1686, 1.0964, 4.1955, 6.0598, 0.5344]\n",
      "w: [1.9845, 2.77, 3.2036, 0.9617, 1.7095, 0.1098, 5.7438, 0.03, 0.9176, 1.5682, 0.4925, 0.7823, 1.8989, 1.2478, 0.158, 1.103, 4.2021, 6.0559, 0.5196]\n",
      "Loss in trainset: 0.2991\n",
      "Loss in testset: 0.3668\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 184379 TEST: 39416\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2827490fd375483587c25bae1b8da226",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/921895 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3319\n",
      "Loss in testset: 0.3195\n",
      "w: [0.8168, 2.0471, 3.0, 1.0, 1.0, 0.2, 6.0, 0.12, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.1039, 2.0, 3.7039, 5.1357, 0.5942]\n",
      "w: [0.8335, 2.0593, 2.6267, 0.802, 1.6501, 0.0071, 6.0814, 0.2628, 1.2292, 1.7333, 0.424, 0.6373, 1.4298, 1.2847, 0.1, 2.1365, 3.7255, 5.5066, 0.5481]\n",
      "Loss in trainset: 0.3140\n",
      "Loss in testset: 0.3024\n",
      "w: [0.5718, 2.513, 2.6035, 0.8099, 1.6919, 0.0, 6.0743, 0.2019, 1.222, 1.675, 0.4771, 0.5967, 1.3621, 1.3265, 0.1044, 2.0737, 3.7738, 5.859, 0.5773]\n",
      "w: [0.343, 2.546, 2.1838, 0.4545, 1.2567, 0.0419, 6.2674, 0.1719, 1.4874, 1.7027, 0.3723, 0.6293, 1.6657, 1.3716, 0.1358, 2.7429, 3.8416, 5.5858, 0.5644]\n",
      "Loss in trainset: 0.3126\n",
      "Loss in testset: 0.3010\n",
      "w: [0.3791, 2.8652, 1.9819, 0.4964, 1.2205, 0.0511, 6.3599, 0.0336, 1.5659, 1.7112, 0.3639, 0.6541, 1.6576, 1.3399, 0.1, 2.856, 3.699, 6.0081, 0.5509]\n",
      "w: [0.1506, 2.939, 2.1053, 0.6634, 1.1219, 0.0678, 6.2499, 0.309, 1.5431, 1.6504, 0.3761, 0.6116, 1.8701, 1.5105, 0.1709, 2.8869, 3.9367, 5.7621, 0.5694]\n",
      "Loss in trainset: 0.3140\n",
      "Loss in testset: 0.3010\n",
      "w: [0.3956, 3.1117, 2.1239, 0.6685, 1.1132, 0.0618, 6.2442, 0.3162, 1.5377, 1.6573, 0.3691, 0.6039, 1.8862, 1.5164, 0.1, 2.8794, 3.9371, 5.8821, 0.5171]\n",
      "w: [0.3669, 3.1559, 2.0962, 0.6003, 1.086, 0.0997, 6.2125, 0.2773, 1.5099, 1.5875, 0.3954, 0.6403, 1.7805, 1.5996, 0.1213, 2.8686, 4.0074, 5.7564, 0.5626]\n",
      "Loss in trainset: 0.3113\n",
      "Loss in testset: 0.3002\n",
      "w: [0.391, 3.1766, 2.0951, 0.5982, 1.0867, 0.1049, 6.2088, 0.2776, 1.5062, 1.5957, 0.3872, 0.6489, 1.783, 1.6042, 0.1189, 2.8601, 3.9989, 5.7863, 0.4807]\n",
      "w: [0.3881, 3.1845, 2.0801, 0.6053, 1.0991, 0.0932, 6.2021, 0.265, 1.499, 1.5858, 0.3901, 0.6477, 1.7785, 1.6216, 0.1049, 2.8572, 4.0113, 5.7807, 0.5149]\n",
      "Loss in trainset: 0.3112\n",
      "Loss in testset: 0.3001\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175532 TEST: 48263\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2ef3e14402b4b958f0be4ee9a2391cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/877660 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3328\n",
      "Loss in testset: 0.3186\n",
      "w: [0.768, 1.9383, 3.0, 1.0, 1.0, 0.2, 6.0, 0.12, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.1, 1.4723, 3.0, 4.9729, 0.6064]\n",
      "w: [0.6788, 2.0367, 2.3239, 0.9385, 1.4685, 0.0329, 5.952, 0.1916, 1.086, 1.8064, 0.357, 0.6747, 1.2325, 1.3096, 0.1095, 1.7554, 3.6344, 5.2406, 0.5563]\n",
      "Loss in trainset: 0.3138\n",
      "Loss in testset: 0.3003\n",
      "w: [0.4167, 2.6167, 2.3295, 0.9244, 1.4891, 0.0556, 5.8986, 0.2068, 1.0365, 1.8199, 0.3429, 0.7408, 1.2195, 1.3479, 0.1, 1.638, 3.6183, 5.7069, 0.7422]\n",
      "w: [0.5852, 2.7349, 2.3131, 0.6791, 1.2956, 0.022, 5.9574, 0.1991, 1.1823, 1.6114, 0.4741, 0.6999, 1.4159, 1.4143, 0.1446, 2.1449, 3.7975, 5.5749, 0.5599]\n",
      "Loss in trainset: 0.3153\n",
      "Loss in testset: 0.3017\n",
      "w: [0.455, 3.0193, 2.2929, 0.5795, 1.308, 0.0454, 6.0007, 0.1892, 1.2273, 1.6414, 0.4436, 0.7011, 1.4537, 1.3772, 0.1, 2.0375, 3.7881, 5.7902, 0.454]\n",
      "w: [0.3802, 3.1267, 2.1987, 0.4567, 1.125, 0.0778, 6.014, 0.2533, 1.2602, 1.5996, 0.3862, 0.6581, 1.5192, 1.4598, 0.1393, 2.4911, 3.9267, 5.5439, 0.5568]\n",
      "Loss in trainset: 0.3124\n",
      "Loss in testset: 0.2997\n",
      "w: [0.3679, 3.3248, 2.1216, 0.4135, 1.0884, 0.0998, 6.054, 0.2122, 1.2969, 1.6367, 0.355, 0.7029, 1.5327, 1.4393, 0.1, 2.4739, 3.8807, 5.7199, 0.4869]\n",
      "w: [0.342, 3.3534, 2.0782, 0.4854, 1.0711, 0.096, 6.0398, 0.2231, 1.2893, 1.5686, 0.3808, 0.646, 1.5112, 1.514, 0.1, 2.6354, 3.9665, 5.5843, 0.5697]\n",
      "Loss in trainset: 0.3122\n",
      "Loss in testset: 0.2994\n",
      "w: [0.3722, 3.3893, 2.0978, 0.4885, 1.0773, 0.0917, 6.0255, 0.233, 1.2756, 1.5659, 0.382, 0.6464, 1.5049, 1.5254, 0.1, 2.6206, 3.9805, 5.5907, 0.4892]\n",
      "w: [0.3797, 3.4017, 2.079, 0.4894, 1.0645, 0.0952, 6.0251, 0.2216, 1.2742, 1.5696, 0.3762, 0.6545, 1.5074, 1.5392, 0.1158, 2.6472, 3.9631, 5.6097, 0.5031]\n",
      "Loss in trainset: 0.3120\n",
      "Loss in testset: 0.2995\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175533 TEST: 48262\n",
      "dataset built\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f39d91740e294934a59e26e6eb270f54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/877665 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3271\n",
      "Loss in testset: 0.3393\n",
      "w: [0.7966, 1.9391, 3.0, 1.0, 1.0, 0.2, 6.0, 0.12, 1.0, 2.0, 0.2, 0.2, 1.0, 1.0, 0.135, 1.493, 3.4738, 5.2212, 0.635]\n",
      "w: [0.7737, 1.968, 2.4462, 0.9399, 1.5026, 0.1011, 6.1595, 0.133, 1.2689, 1.8758, 0.392, 0.7631, 1.5049, 1.4037, 0.1667, 1.8881, 3.7524, 5.4906, 0.5618]\n",
      "Loss in trainset: 0.3077\n",
      "Loss in testset: 0.3239\n",
      "w: [0.4846, 2.4553, 2.4466, 0.9117, 1.4705, 0.1388, 6.1133, 0.2056, 1.2262, 1.9063, 0.3657, 0.8209, 1.5205, 1.4159, 0.1, 1.7057, 3.7378, 5.9331, 0.6319]\n",
      "w: [0.223, 2.5511, 2.0479, 0.4324, 1.4093, 0.0151, 6.3777, 0.2249, 1.561, 1.7933, 0.4225, 0.6209, 1.9304, 1.3741, 0.132, 2.5005, 3.9922, 5.6204, 0.5903]\n",
      "Loss in trainset: 0.3079\n",
      "Loss in testset: 0.3236\n",
      "w: [0.4144, 2.8654, 1.9992, 0.3592, 1.4328, 0.007, 6.4199, 0.2112, 1.6017, 1.8001, 0.413, 0.626, 1.938, 1.3456, 0.1, 2.4236, 4.0349, 5.8556, 0.6537]\n",
      "w: [0.1391, 2.9196, 2.2357, 0.6391, 1.2602, 0.1027, 6.2829, 0.2453, 1.543, 1.7953, 0.3942, 0.6594, 1.9691, 1.5136, 0.1223, 2.6436, 4.2065, 5.5942, 0.5336]\n",
      "Loss in trainset: 0.3081\n",
      "Loss in testset: 0.3237\n",
      "w: [0.3762, 3.0742, 2.2077, 0.6806, 1.2696, 0.0701, 6.2921, 0.194, 1.5503, 1.764, 0.4232, 0.6343, 1.9278, 1.5264, 0.1, 2.6655, 4.182, 5.7674, 0.4955]\n",
      "w: [0.3768, 3.1145, 2.2516, 0.5493, 1.2135, 0.119, 6.2692, 0.2516, 1.5323, 1.7515, 0.3976, 0.7008, 1.9171, 1.5628, 0.1109, 2.8292, 4.1682, 5.6925, 0.562]\n",
      "Loss in trainset: 0.3061\n",
      "Loss in testset: 0.3230\n",
      "w: [0.3989, 3.1327, 2.249, 0.5387, 1.2064, 0.1309, 6.2728, 0.2564, 1.5361, 1.7539, 0.3949, 0.7008, 1.9232, 1.5581, 0.1189, 2.8207, 4.1504, 5.7266, 0.4836]\n",
      "w: [0.387, 3.1374, 2.2302, 0.5681, 1.2321, 0.1055, 6.2594, 0.2383, 1.522, 1.7194, 0.4243, 0.6889, 1.8882, 1.5875, 0.1006, 2.8289, 4.1522, 5.7459, 0.5269]\n",
      "Loss in trainset: 0.3057\n",
      "Loss in testset: 0.3225\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=5)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=5, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7053, 2.9876, 2.3334, 0.6475, 1.2737, 0.1001, 6.0993, 0.1988, 1.327, 1.5937, 0.4109, 0.694, 1.6977, 1.5098, 0.1202, 2.3851, 4.0326, 5.6991, 0.5172]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,1,1,3,5,9,15,26,43,71,114\n",
      "difficulty history: 0,4.9,4.6,4.4,4.2,4.0,3.8,3.7,3.5,3.4,3.3\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,7,14,27,49,85,144,237,380,595,909\n",
      "difficulty history: 0,3.4,3.3,3.2,3.1,3.0,3.0,2.9,2.8,2.8,2.7\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,12,30,67,137,261,469,801,1312,2070,3163\n",
      "difficulty history: 0,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,17,70,217,551,1204,2349,4200,7008,11065,16701\n",
      "difficulty history: 0,1.3,1.4,1.5,1.5,1.6,1.7,1.8,1.8,1.9,1.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = next_interval(requestRetention, float(states[0]), avg_w[18])\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(f\"interval history: {t_history}\")\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([12.3940,  2.3334])\n",
      "tensor([30.1861,  2.3334])\n",
      "tensor([66.9914,  2.3334])\n",
      "tensor([136.9322,   2.3334])\n",
      "tensor([260.9762,   2.3334])\n",
      "tensor([40.4634,  6.8178])\n",
      "tensor([ 8.9752, 10.8533])\n",
      "tensor([10.5299, 10.0005])\n",
      "tensor([12.5666,  9.2330])\n",
      "tensor([15.1920,  8.5423])\n",
      "tensor([18.5350,  7.9208])\n",
      "tensor([23.0007,  7.3615])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,12,30,67,137,261,40,9,11,13,15,19,23\n",
      "difficulty history: 0,2.3,2.3,2.3,2.3,2.3,6.8,10.9,10.0,9.2,8.5,7.9,7.4\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = next_interval(requestRetention, float(states[0]), avg_w[18])\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.171219\n",
      "2     0.155611\n",
      "3     0.042450\n",
      "4     0.061833\n",
      "5     0.087835\n",
      "6     0.070109\n",
      "7     0.052678\n",
      "8     0.060591\n",
      "9     0.059756\n",
      "10    0.052066\n",
      "11    0.044755\n",
      "12    0.041878\n",
      "13    0.036306\n",
      "14    0.032311\n",
      "15    0.030604\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(15)\n",
    "for i in range(15):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce2dd2d13f29428a9184a943eef5f889",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.76-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 15\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + avg_w[16] - avg_w[14]) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def post_lapse_stability_bonus(s):\n",
    "    offset = np.power(1/avg_w[11],1/(avg_w[11]-1))\n",
    "    return np.power(s+offset, avg_w[11]) - np.power(offset, avg_w[11])\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + avg_w[6] * 10 * np.power(d, -avg_w[13]) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * post_lapse_stability_bonus(s) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = forgetting_curve(stability, interval, avg_w[18])\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 15)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(15)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3298\n",
      "Loss after training: 0.3098\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['stability'], dataset['delta_t'], init_w[18])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = forgetting_curve(dataset['stability'], dataset['delta_t'], avg_w[18])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9706\n",
      "RMSE: 0.0119\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration')\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration')\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fb75b_row0_col0, #T_fb75b_row0_col1, #T_fb75b_row0_col2, #T_fb75b_row0_col3, #T_fb75b_row0_col4, #T_fb75b_row0_col5, #T_fb75b_row0_col6, #T_fb75b_row0_col7, #T_fb75b_row0_col8, #T_fb75b_row0_col9, #T_fb75b_row0_col10, #T_fb75b_row0_col11, #T_fb75b_row0_col12, #T_fb75b_row0_col13, #T_fb75b_row1_col0, #T_fb75b_row1_col1, #T_fb75b_row1_col2, #T_fb75b_row1_col3, #T_fb75b_row1_col4, #T_fb75b_row1_col5, #T_fb75b_row1_col6, #T_fb75b_row1_col7, #T_fb75b_row1_col8, #T_fb75b_row1_col9, #T_fb75b_row1_col10, #T_fb75b_row1_col11, #T_fb75b_row1_col13, #T_fb75b_row2_col0, #T_fb75b_row2_col1, #T_fb75b_row2_col2, #T_fb75b_row2_col3, #T_fb75b_row2_col4, #T_fb75b_row2_col5, #T_fb75b_row2_col6, #T_fb75b_row2_col7, #T_fb75b_row2_col9, #T_fb75b_row2_col10, #T_fb75b_row2_col12, #T_fb75b_row3_col0, #T_fb75b_row3_col1, #T_fb75b_row3_col2, #T_fb75b_row3_col3, #T_fb75b_row3_col5, #T_fb75b_row3_col6, #T_fb75b_row3_col7, #T_fb75b_row3_col9, #T_fb75b_row3_col11, #T_fb75b_row4_col0, #T_fb75b_row4_col1, #T_fb75b_row4_col2, #T_fb75b_row4_col3, #T_fb75b_row4_col4, #T_fb75b_row4_col5, #T_fb75b_row4_col6, #T_fb75b_row5_col0, #T_fb75b_row5_col1, #T_fb75b_row5_col2, #T_fb75b_row5_col3, #T_fb75b_row5_col4, #T_fb75b_row5_col5, #T_fb75b_row5_col8, #T_fb75b_row6_col0, #T_fb75b_row6_col1, #T_fb75b_row6_col2, #T_fb75b_row6_col3, #T_fb75b_row6_col5, #T_fb75b_row7_col0, #T_fb75b_row7_col1, #T_fb75b_row7_col2, #T_fb75b_row7_col3, #T_fb75b_row8_col0, #T_fb75b_row8_col1, #T_fb75b_row8_col2, #T_fb75b_row8_col4, #T_fb75b_row9_col0, #T_fb75b_row9_col1, #T_fb75b_row9_col2, #T_fb75b_row9_col4, #T_fb75b_row9_col14, #T_fb75b_row10_col0, #T_fb75b_row10_col1, #T_fb75b_row10_col13, #T_fb75b_row10_col14, #T_fb75b_row11_col0, #T_fb75b_row11_col1, #T_fb75b_row11_col2, #T_fb75b_row11_col12, #T_fb75b_row11_col13, #T_fb75b_row11_col14, #T_fb75b_row12_col0, #T_fb75b_row12_col2, #T_fb75b_row12_col11, #T_fb75b_row12_col12, #T_fb75b_row12_col13, #T_fb75b_row12_col14, #T_fb75b_row13_col10, #T_fb75b_row13_col11, #T_fb75b_row13_col12, #T_fb75b_row13_col13, #T_fb75b_row13_col14, #T_fb75b_row14_col0, #T_fb75b_row14_col9, #T_fb75b_row14_col10, #T_fb75b_row14_col11, #T_fb75b_row14_col12, #T_fb75b_row14_col13, #T_fb75b_row14_col14, #T_fb75b_row15_col9, #T_fb75b_row15_col10, #T_fb75b_row15_col11, #T_fb75b_row15_col12, #T_fb75b_row15_col13, #T_fb75b_row15_col14, #T_fb75b_row16_col8, #T_fb75b_row16_col9, #T_fb75b_row16_col10, #T_fb75b_row16_col11, #T_fb75b_row16_col12, #T_fb75b_row16_col13, #T_fb75b_row16_col14, #T_fb75b_row17_col6, #T_fb75b_row17_col7, #T_fb75b_row17_col8, #T_fb75b_row17_col9, #T_fb75b_row17_col10, #T_fb75b_row17_col11, #T_fb75b_row17_col12, #T_fb75b_row17_col13, #T_fb75b_row17_col14, #T_fb75b_row18_col5, #T_fb75b_row18_col6, #T_fb75b_row18_col7, #T_fb75b_row18_col8, #T_fb75b_row18_col9, #T_fb75b_row18_col10, #T_fb75b_row18_col11, #T_fb75b_row18_col12, #T_fb75b_row18_col13, #T_fb75b_row18_col14, #T_fb75b_row19_col3, #T_fb75b_row19_col4, #T_fb75b_row19_col5, #T_fb75b_row19_col6, #T_fb75b_row19_col7, #T_fb75b_row19_col8, #T_fb75b_row19_col9, #T_fb75b_row19_col10, #T_fb75b_row19_col11, #T_fb75b_row19_col12, #T_fb75b_row19_col13, #T_fb75b_row19_col14, #T_fb75b_row20_col4, #T_fb75b_row20_col5, #T_fb75b_row20_col6, #T_fb75b_row20_col7, #T_fb75b_row20_col8, #T_fb75b_row20_col9, #T_fb75b_row20_col10, #T_fb75b_row20_col11, #T_fb75b_row20_col12, #T_fb75b_row20_col13, #T_fb75b_row20_col14, #T_fb75b_row21_col3, #T_fb75b_row21_col4, #T_fb75b_row21_col5, #T_fb75b_row21_col6, #T_fb75b_row21_col7, #T_fb75b_row21_col8, #T_fb75b_row21_col9, #T_fb75b_row21_col10, #T_fb75b_row21_col11, #T_fb75b_row21_col12, #T_fb75b_row21_col13, #T_fb75b_row21_col14, #T_fb75b_row22_col3, #T_fb75b_row22_col4, #T_fb75b_row22_col5, #T_fb75b_row22_col6, #T_fb75b_row22_col7, #T_fb75b_row22_col8, #T_fb75b_row22_col9, #T_fb75b_row22_col10, #T_fb75b_row22_col11, #T_fb75b_row22_col12, #T_fb75b_row22_col13, #T_fb75b_row22_col14, #T_fb75b_row23_col3, #T_fb75b_row23_col4, #T_fb75b_row23_col5, #T_fb75b_row23_col6, #T_fb75b_row23_col7, #T_fb75b_row23_col8, #T_fb75b_row23_col9, #T_fb75b_row23_col10, #T_fb75b_row23_col11, #T_fb75b_row23_col12, #T_fb75b_row23_col13, #T_fb75b_row23_col14 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row0_col14, #T_fb75b_row13_col7, #T_fb75b_row14_col7, #T_fb75b_row16_col3 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row1_col12 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row1_col14, #T_fb75b_row2_col14, #T_fb75b_row4_col11, #T_fb75b_row7_col5 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row2_col8 {\n",
       "  background-color: #ff2121;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row2_col11 {\n",
       "  background-color: #ff4d4d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row2_col13, #T_fb75b_row12_col3, #T_fb75b_row20_col0 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row3_col4 {\n",
       "  background-color: #ff6161;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row3_col8 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row3_col10, #T_fb75b_row6_col10, #T_fb75b_row7_col11, #T_fb75b_row9_col8, #T_fb75b_row11_col8, #T_fb75b_row14_col1, #T_fb75b_row16_col0 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row3_col12, #T_fb75b_row20_col3 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row3_col13, #T_fb75b_row8_col9, #T_fb75b_row10_col7, #T_fb75b_row12_col8, #T_fb75b_row15_col3, #T_fb75b_row15_col8, #T_fb75b_row16_col7, #T_fb75b_row21_col0 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row3_col14, #T_fb75b_row13_col4 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row4_col7, #T_fb75b_row4_col14, #T_fb75b_row10_col8, #T_fb75b_row11_col5, #T_fb75b_row12_col1, #T_fb75b_row12_col9, #T_fb75b_row14_col3, #T_fb75b_row14_col5 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row4_col8, #T_fb75b_row10_col9, #T_fb75b_row11_col4 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row4_col9, #T_fb75b_row5_col9, #T_fb75b_row6_col11, #T_fb75b_row7_col12, #T_fb75b_row8_col13, #T_fb75b_row9_col11, #T_fb75b_row12_col10, #T_fb75b_row15_col5, #T_fb75b_row16_col4, #T_fb75b_row18_col0, #T_fb75b_row19_col0 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row4_col10, #T_fb75b_row4_col12, #T_fb75b_row9_col12, #T_fb75b_row10_col12 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row4_col13, #T_fb75b_row7_col7 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row5_col6 {\n",
       "  background-color: #ff1111;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row5_col7, #T_fb75b_row7_col6, #T_fb75b_row10_col10, #T_fb75b_row17_col4 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row5_col10 {\n",
       "  background-color: #8585ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row5_col11, #T_fb75b_row8_col14, #T_fb75b_row10_col11, #T_fb75b_row11_col9, #T_fb75b_row12_col7, #T_fb75b_row13_col6, #T_fb75b_row14_col6, #T_fb75b_row15_col4, #T_fb75b_row17_col2, #T_fb75b_row21_col2 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row5_col12, #T_fb75b_row6_col12, #T_fb75b_row6_col13, #T_fb75b_row21_col1 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row5_col13, #T_fb75b_row8_col3, #T_fb75b_row12_col4 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row5_col14, #T_fb75b_row6_col7, #T_fb75b_row6_col14, #T_fb75b_row9_col10, #T_fb75b_row15_col7, #T_fb75b_row19_col1 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row6_col4, #T_fb75b_row7_col8, #T_fb75b_row11_col3, #T_fb75b_row11_col7, #T_fb75b_row16_col5, #T_fb75b_row16_col6 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row6_col6, #T_fb75b_row12_col6, #T_fb75b_row16_col2, #T_fb75b_row22_col2 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row6_col8 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row6_col9, #T_fb75b_row7_col10, #T_fb75b_row11_col6, #T_fb75b_row13_col1, #T_fb75b_row15_col2, #T_fb75b_row17_col0 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row7_col4 {\n",
       "  background-color: #2121ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row7_col9, #T_fb75b_row10_col6 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row7_col13, #T_fb75b_row8_col7, #T_fb75b_row8_col11, #T_fb75b_row9_col3, #T_fb75b_row18_col3 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row7_col14, #T_fb75b_row12_col5 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row8_col5 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row8_col6 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row8_col8, #T_fb75b_row9_col5, #T_fb75b_row13_col0, #T_fb75b_row13_col9, #T_fb75b_row15_col0, #T_fb75b_row20_col1, #T_fb75b_row22_col0, #T_fb75b_row23_col1 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row8_col10, #T_fb75b_row9_col13, #T_fb75b_row10_col4, #T_fb75b_row11_col10, #T_fb75b_row13_col3, #T_fb75b_row13_col5, #T_fb75b_row15_col6, #T_fb75b_row18_col1 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row8_col12, #T_fb75b_row9_col9, #T_fb75b_row16_col1 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row9_col6, #T_fb75b_row18_col4, #T_fb75b_row19_col2 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row9_col7, #T_fb75b_row10_col5, #T_fb75b_row17_col1 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row10_col2 {\n",
       "  background-color: #ff7575;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fb75b_row10_col3 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row11_col11, #T_fb75b_row14_col2, #T_fb75b_row14_col8, #T_fb75b_row15_col1, #T_fb75b_row17_col3, #T_fb75b_row18_col2 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row13_col2 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row13_col8, #T_fb75b_row17_col5 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row14_col4 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row20_col2 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row22_col1 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row23_col0 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fb75b_row23_col2 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fb75b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th class=\"col_heading level0 col0\" >1</th>\n",
       "      <th class=\"col_heading level0 col1\" >2</th>\n",
       "      <th class=\"col_heading level0 col2\" >3</th>\n",
       "      <th class=\"col_heading level0 col3\" >4</th>\n",
       "      <th class=\"col_heading level0 col4\" >5</th>\n",
       "      <th class=\"col_heading level0 col5\" >6</th>\n",
       "      <th class=\"col_heading level0 col6\" >7</th>\n",
       "      <th class=\"col_heading level0 col7\" >8</th>\n",
       "      <th class=\"col_heading level0 col8\" >9</th>\n",
       "      <th class=\"col_heading level0 col9\" >10</th>\n",
       "      <th class=\"col_heading level0 col10\" >11</th>\n",
       "      <th class=\"col_heading level0 col11\" >12</th>\n",
       "      <th class=\"col_heading level0 col12\" >13</th>\n",
       "      <th class=\"col_heading level0 col13\" >14</th>\n",
       "      <th class=\"col_heading level0 col14\" >15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row0\" class=\"row_heading level0 row0\" >0.19</th>\n",
       "      <td id=\"T_fb75b_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col9\" class=\"data row0 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col10\" class=\"data row0 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col11\" class=\"data row0 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col12\" class=\"data row0 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col13\" class=\"data row0 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row0_col14\" class=\"data row0 col14\" >2.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row1\" class=\"row_heading level0 row1\" >0.26</th>\n",
       "      <td id=\"T_fb75b_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col9\" class=\"data row1 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col10\" class=\"data row1 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col11\" class=\"data row1 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col12\" class=\"data row1 col12\" >3.06%</td>\n",
       "      <td id=\"T_fb75b_row1_col13\" class=\"data row1 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row1_col14\" class=\"data row1 col14\" >1.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row2\" class=\"row_heading level0 row2\" >0.36</th>\n",
       "      <td id=\"T_fb75b_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col8\" class=\"data row2 col8\" >8.65%</td>\n",
       "      <td id=\"T_fb75b_row2_col9\" class=\"data row2 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col10\" class=\"data row2 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col11\" class=\"data row2 col11\" >6.96%</td>\n",
       "      <td id=\"T_fb75b_row2_col12\" class=\"data row2 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row2_col13\" class=\"data row2 col13\" >1.11%</td>\n",
       "      <td id=\"T_fb75b_row2_col14\" class=\"data row2 col14\" >1.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row3\" class=\"row_heading level0 row3\" >0.51</th>\n",
       "      <td id=\"T_fb75b_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col4\" class=\"data row3 col4\" >6.18%</td>\n",
       "      <td id=\"T_fb75b_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col7\" class=\"data row3 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col8\" class=\"data row3 col8\" >-1.69%</td>\n",
       "      <td id=\"T_fb75b_row3_col9\" class=\"data row3 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col10\" class=\"data row3 col10\" >-1.43%</td>\n",
       "      <td id=\"T_fb75b_row3_col11\" class=\"data row3 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row3_col12\" class=\"data row3 col12\" >2.81%</td>\n",
       "      <td id=\"T_fb75b_row3_col13\" class=\"data row3 col13\" >0.02%</td>\n",
       "      <td id=\"T_fb75b_row3_col14\" class=\"data row3 col14\" >0.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row4\" class=\"row_heading level0 row4\" >0.71</th>\n",
       "      <td id=\"T_fb75b_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col6\" class=\"data row4 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row4_col7\" class=\"data row4 col7\" >0.39%</td>\n",
       "      <td id=\"T_fb75b_row4_col8\" class=\"data row4 col8\" >0.54%</td>\n",
       "      <td id=\"T_fb75b_row4_col9\" class=\"data row4 col9\" >-0.57%</td>\n",
       "      <td id=\"T_fb75b_row4_col10\" class=\"data row4 col10\" >1.27%</td>\n",
       "      <td id=\"T_fb75b_row4_col11\" class=\"data row4 col11\" >1.50%</td>\n",
       "      <td id=\"T_fb75b_row4_col12\" class=\"data row4 col12\" >1.27%</td>\n",
       "      <td id=\"T_fb75b_row4_col13\" class=\"data row4 col13\" >-2.96%</td>\n",
       "      <td id=\"T_fb75b_row4_col14\" class=\"data row4 col14\" >0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row5\" class=\"row_heading level0 row5\" >1.0</th>\n",
       "      <td id=\"T_fb75b_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col6\" class=\"data row5 col6\" >9.26%</td>\n",
       "      <td id=\"T_fb75b_row5_col7\" class=\"data row5 col7\" >-2.13%</td>\n",
       "      <td id=\"T_fb75b_row5_col8\" class=\"data row5 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row5_col9\" class=\"data row5 col9\" >-0.48%</td>\n",
       "      <td id=\"T_fb75b_row5_col10\" class=\"data row5 col10\" >-4.80%</td>\n",
       "      <td id=\"T_fb75b_row5_col11\" class=\"data row5 col11\" >0.67%</td>\n",
       "      <td id=\"T_fb75b_row5_col12\" class=\"data row5 col12\" >-1.82%</td>\n",
       "      <td id=\"T_fb75b_row5_col13\" class=\"data row5 col13\" >-1.95%</td>\n",
       "      <td id=\"T_fb75b_row5_col14\" class=\"data row5 col14\" >-0.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row6\" class=\"row_heading level0 row6\" >1.4</th>\n",
       "      <td id=\"T_fb75b_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row6_col4\" class=\"data row6 col4\" >-0.63%</td>\n",
       "      <td id=\"T_fb75b_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row6_col6\" class=\"data row6 col6\" >-0.37%</td>\n",
       "      <td id=\"T_fb75b_row6_col7\" class=\"data row6 col7\" >-0.89%</td>\n",
       "      <td id=\"T_fb75b_row6_col8\" class=\"data row6 col8\" >-2.99%</td>\n",
       "      <td id=\"T_fb75b_row6_col9\" class=\"data row6 col9\" >-1.17%</td>\n",
       "      <td id=\"T_fb75b_row6_col10\" class=\"data row6 col10\" >-1.45%</td>\n",
       "      <td id=\"T_fb75b_row6_col11\" class=\"data row6 col11\" >-0.47%</td>\n",
       "      <td id=\"T_fb75b_row6_col12\" class=\"data row6 col12\" >-1.83%</td>\n",
       "      <td id=\"T_fb75b_row6_col13\" class=\"data row6 col13\" >-1.83%</td>\n",
       "      <td id=\"T_fb75b_row6_col14\" class=\"data row6 col14\" >-0.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row7\" class=\"row_heading level0 row7\" >1.96</th>\n",
       "      <td id=\"T_fb75b_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row7_col4\" class=\"data row7 col4\" >-8.70%</td>\n",
       "      <td id=\"T_fb75b_row7_col5\" class=\"data row7 col5\" >1.47%</td>\n",
       "      <td id=\"T_fb75b_row7_col6\" class=\"data row7 col6\" >-2.13%</td>\n",
       "      <td id=\"T_fb75b_row7_col7\" class=\"data row7 col7\" >-2.96%</td>\n",
       "      <td id=\"T_fb75b_row7_col8\" class=\"data row7 col8\" >-0.71%</td>\n",
       "      <td id=\"T_fb75b_row7_col9\" class=\"data row7 col9\" >-2.34%</td>\n",
       "      <td id=\"T_fb75b_row7_col10\" class=\"data row7 col10\" >-1.19%</td>\n",
       "      <td id=\"T_fb75b_row7_col11\" class=\"data row7 col11\" >-1.45%</td>\n",
       "      <td id=\"T_fb75b_row7_col12\" class=\"data row7 col12\" >-0.52%</td>\n",
       "      <td id=\"T_fb75b_row7_col13\" class=\"data row7 col13\" >-0.94%</td>\n",
       "      <td id=\"T_fb75b_row7_col14\" class=\"data row7 col14\" >0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row8\" class=\"row_heading level0 row8\" >2.74</th>\n",
       "      <td id=\"T_fb75b_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row8_col3\" class=\"data row8 col3\" >-2.03%</td>\n",
       "      <td id=\"T_fb75b_row8_col4\" class=\"data row8 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row8_col5\" class=\"data row8 col5\" >-4.33%</td>\n",
       "      <td id=\"T_fb75b_row8_col6\" class=\"data row8 col6\" >-2.25%</td>\n",
       "      <td id=\"T_fb75b_row8_col7\" class=\"data row8 col7\" >-1.02%</td>\n",
       "      <td id=\"T_fb75b_row8_col8\" class=\"data row8 col8\" >-1.28%</td>\n",
       "      <td id=\"T_fb75b_row8_col9\" class=\"data row8 col9\" >0.08%</td>\n",
       "      <td id=\"T_fb75b_row8_col10\" class=\"data row8 col10\" >-0.06%</td>\n",
       "      <td id=\"T_fb75b_row8_col11\" class=\"data row8 col11\" >-1.04%</td>\n",
       "      <td id=\"T_fb75b_row8_col12\" class=\"data row8 col12\" >-0.17%</td>\n",
       "      <td id=\"T_fb75b_row8_col13\" class=\"data row8 col13\" >-0.52%</td>\n",
       "      <td id=\"T_fb75b_row8_col14\" class=\"data row8 col14\" >0.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row9\" class=\"row_heading level0 row9\" >3.84</th>\n",
       "      <td id=\"T_fb75b_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row9_col3\" class=\"data row9 col3\" >-0.96%</td>\n",
       "      <td id=\"T_fb75b_row9_col4\" class=\"data row9 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row9_col5\" class=\"data row9 col5\" >-1.27%</td>\n",
       "      <td id=\"T_fb75b_row9_col6\" class=\"data row9 col6\" >2.27%</td>\n",
       "      <td id=\"T_fb75b_row9_col7\" class=\"data row9 col7\" >1.05%</td>\n",
       "      <td id=\"T_fb75b_row9_col8\" class=\"data row9 col8\" >-1.41%</td>\n",
       "      <td id=\"T_fb75b_row9_col9\" class=\"data row9 col9\" >-0.26%</td>\n",
       "      <td id=\"T_fb75b_row9_col10\" class=\"data row9 col10\" >-0.84%</td>\n",
       "      <td id=\"T_fb75b_row9_col11\" class=\"data row9 col11\" >-0.56%</td>\n",
       "      <td id=\"T_fb75b_row9_col12\" class=\"data row9 col12\" >1.29%</td>\n",
       "      <td id=\"T_fb75b_row9_col13\" class=\"data row9 col13\" >-0.14%</td>\n",
       "      <td id=\"T_fb75b_row9_col14\" class=\"data row9 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row10\" class=\"row_heading level0 row10\" >5.38</th>\n",
       "      <td id=\"T_fb75b_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row10_col2\" class=\"data row10 col2\" >5.32%</td>\n",
       "      <td id=\"T_fb75b_row10_col3\" class=\"data row10 col3\" >-2.66%</td>\n",
       "      <td id=\"T_fb75b_row10_col4\" class=\"data row10 col4\" >-0.04%</td>\n",
       "      <td id=\"T_fb75b_row10_col5\" class=\"data row10 col5\" >1.01%</td>\n",
       "      <td id=\"T_fb75b_row10_col6\" class=\"data row10 col6\" >-2.43%</td>\n",
       "      <td id=\"T_fb75b_row10_col7\" class=\"data row10 col7\" >0.04%</td>\n",
       "      <td id=\"T_fb75b_row10_col8\" class=\"data row10 col8\" >0.33%</td>\n",
       "      <td id=\"T_fb75b_row10_col9\" class=\"data row10 col9\" >0.58%</td>\n",
       "      <td id=\"T_fb75b_row10_col10\" class=\"data row10 col10\" >-2.14%</td>\n",
       "      <td id=\"T_fb75b_row10_col11\" class=\"data row10 col11\" >0.72%</td>\n",
       "      <td id=\"T_fb75b_row10_col12\" class=\"data row10 col12\" >1.25%</td>\n",
       "      <td id=\"T_fb75b_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row10_col14\" class=\"data row10 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row11\" class=\"row_heading level0 row11\" >7.53</th>\n",
       "      <td id=\"T_fb75b_row11_col0\" class=\"data row11 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_fb75b_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row11_col3\" class=\"data row11 col3\" >-0.78%</td>\n",
       "      <td id=\"T_fb75b_row11_col4\" class=\"data row11 col4\" >0.58%</td>\n",
       "      <td id=\"T_fb75b_row11_col5\" class=\"data row11 col5\" >0.39%</td>\n",
       "      <td id=\"T_fb75b_row11_col6\" class=\"data row11 col6\" >-1.15%</td>\n",
       "      <td id=\"T_fb75b_row11_col7\" class=\"data row11 col7\" >-0.76%</td>\n",
       "      <td id=\"T_fb75b_row11_col8\" class=\"data row11 col8\" >-1.54%</td>\n",
       "      <td id=\"T_fb75b_row11_col9\" class=\"data row11 col9\" >0.76%</td>\n",
       "      <td id=\"T_fb75b_row11_col10\" class=\"data row11 col10\" >-0.01%</td>\n",
       "      <td id=\"T_fb75b_row11_col11\" class=\"data row11 col11\" >1.73%</td>\n",
       "      <td id=\"T_fb75b_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row11_col14\" class=\"data row11 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row12\" class=\"row_heading level0 row12\" >10.54</th>\n",
       "      <td id=\"T_fb75b_row12_col0\" class=\"data row12 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row12_col1\" class=\"data row12 col1\" >0.38%</td>\n",
       "      <td id=\"T_fb75b_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "      <td id=\"T_fb75b_row12_col3\" class=\"data row12 col3\" >1.19%</td>\n",
       "      <td id=\"T_fb75b_row12_col4\" class=\"data row12 col4\" >-1.99%</td>\n",
       "      <td id=\"T_fb75b_row12_col5\" class=\"data row12 col5\" >0.24%</td>\n",
       "      <td id=\"T_fb75b_row12_col6\" class=\"data row12 col6\" >-0.34%</td>\n",
       "      <td id=\"T_fb75b_row12_col7\" class=\"data row12 col7\" >0.64%</td>\n",
       "      <td id=\"T_fb75b_row12_col8\" class=\"data row12 col8\" >0.14%</td>\n",
       "      <td id=\"T_fb75b_row12_col9\" class=\"data row12 col9\" >0.44%</td>\n",
       "      <td id=\"T_fb75b_row12_col10\" class=\"data row12 col10\" >-0.53%</td>\n",
       "      <td id=\"T_fb75b_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row12_col14\" class=\"data row12 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row13\" class=\"row_heading level0 row13\" >14.76</th>\n",
       "      <td id=\"T_fb75b_row13_col0\" class=\"data row13 col0\" >-1.25%</td>\n",
       "      <td id=\"T_fb75b_row13_col1\" class=\"data row13 col1\" >-1.12%</td>\n",
       "      <td id=\"T_fb75b_row13_col2\" class=\"data row13 col2\" >3.74%</td>\n",
       "      <td id=\"T_fb75b_row13_col3\" class=\"data row13 col3\" >-0.14%</td>\n",
       "      <td id=\"T_fb75b_row13_col4\" class=\"data row13 col4\" >0.81%</td>\n",
       "      <td id=\"T_fb75b_row13_col5\" class=\"data row13 col5\" >-0.05%</td>\n",
       "      <td id=\"T_fb75b_row13_col6\" class=\"data row13 col6\" >0.76%</td>\n",
       "      <td id=\"T_fb75b_row13_col7\" class=\"data row13 col7\" >2.15%</td>\n",
       "      <td id=\"T_fb75b_row13_col8\" class=\"data row13 col8\" >2.43%</td>\n",
       "      <td id=\"T_fb75b_row13_col9\" class=\"data row13 col9\" >-1.32%</td>\n",
       "      <td id=\"T_fb75b_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row13_col14\" class=\"data row13 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row14\" class=\"row_heading level0 row14\" >20.66</th>\n",
       "      <td id=\"T_fb75b_row14_col0\" class=\"data row14 col0\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col1\" class=\"data row14 col1\" >-1.51%</td>\n",
       "      <td id=\"T_fb75b_row14_col2\" class=\"data row14 col2\" >1.73%</td>\n",
       "      <td id=\"T_fb75b_row14_col3\" class=\"data row14 col3\" >0.37%</td>\n",
       "      <td id=\"T_fb75b_row14_col4\" class=\"data row14 col4\" >1.59%</td>\n",
       "      <td id=\"T_fb75b_row14_col5\" class=\"data row14 col5\" >0.35%</td>\n",
       "      <td id=\"T_fb75b_row14_col6\" class=\"data row14 col6\" >0.71%</td>\n",
       "      <td id=\"T_fb75b_row14_col7\" class=\"data row14 col7\" >2.15%</td>\n",
       "      <td id=\"T_fb75b_row14_col8\" class=\"data row14 col8\" >1.80%</td>\n",
       "      <td id=\"T_fb75b_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col10\" class=\"data row14 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col11\" class=\"data row14 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col12\" class=\"data row14 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col13\" class=\"data row14 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row14_col14\" class=\"data row14 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row15\" class=\"row_heading level0 row15\" >28.93</th>\n",
       "      <td id=\"T_fb75b_row15_col0\" class=\"data row15 col0\" >-1.29%</td>\n",
       "      <td id=\"T_fb75b_row15_col1\" class=\"data row15 col1\" >1.84%</td>\n",
       "      <td id=\"T_fb75b_row15_col2\" class=\"data row15 col2\" >-1.11%</td>\n",
       "      <td id=\"T_fb75b_row15_col3\" class=\"data row15 col3\" >0.02%</td>\n",
       "      <td id=\"T_fb75b_row15_col4\" class=\"data row15 col4\" >0.77%</td>\n",
       "      <td id=\"T_fb75b_row15_col5\" class=\"data row15 col5\" >-0.60%</td>\n",
       "      <td id=\"T_fb75b_row15_col6\" class=\"data row15 col6\" >-0.10%</td>\n",
       "      <td id=\"T_fb75b_row15_col7\" class=\"data row15 col7\" >-0.90%</td>\n",
       "      <td id=\"T_fb75b_row15_col8\" class=\"data row15 col8\" >0.04%</td>\n",
       "      <td id=\"T_fb75b_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row15_col10\" class=\"data row15 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row15_col11\" class=\"data row15 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row15_col12\" class=\"data row15 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row15_col13\" class=\"data row15 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row15_col14\" class=\"data row15 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row16\" class=\"row_heading level0 row16\" >40.5</th>\n",
       "      <td id=\"T_fb75b_row16_col0\" class=\"data row16 col0\" >-1.42%</td>\n",
       "      <td id=\"T_fb75b_row16_col1\" class=\"data row16 col1\" >-0.27%</td>\n",
       "      <td id=\"T_fb75b_row16_col2\" class=\"data row16 col2\" >-0.32%</td>\n",
       "      <td id=\"T_fb75b_row16_col3\" class=\"data row16 col3\" >2.12%</td>\n",
       "      <td id=\"T_fb75b_row16_col4\" class=\"data row16 col4\" >-0.52%</td>\n",
       "      <td id=\"T_fb75b_row16_col5\" class=\"data row16 col5\" >-0.75%</td>\n",
       "      <td id=\"T_fb75b_row16_col6\" class=\"data row16 col6\" >-0.68%</td>\n",
       "      <td id=\"T_fb75b_row16_col7\" class=\"data row16 col7\" >0.09%</td>\n",
       "      <td id=\"T_fb75b_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col10\" class=\"data row16 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col11\" class=\"data row16 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col12\" class=\"data row16 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col13\" class=\"data row16 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row16_col14\" class=\"data row16 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row17\" class=\"row_heading level0 row17\" >56.69</th>\n",
       "      <td id=\"T_fb75b_row17_col0\" class=\"data row17 col0\" >-1.23%</td>\n",
       "      <td id=\"T_fb75b_row17_col1\" class=\"data row17 col1\" >0.99%</td>\n",
       "      <td id=\"T_fb75b_row17_col2\" class=\"data row17 col2\" >0.74%</td>\n",
       "      <td id=\"T_fb75b_row17_col3\" class=\"data row17 col3\" >1.75%</td>\n",
       "      <td id=\"T_fb75b_row17_col4\" class=\"data row17 col4\" >-2.06%</td>\n",
       "      <td id=\"T_fb75b_row17_col5\" class=\"data row17 col5\" >2.48%</td>\n",
       "      <td id=\"T_fb75b_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col10\" class=\"data row17 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col11\" class=\"data row17 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col12\" class=\"data row17 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col13\" class=\"data row17 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row17_col14\" class=\"data row17 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row18\" class=\"row_heading level0 row18\" >79.37</th>\n",
       "      <td id=\"T_fb75b_row18_col0\" class=\"data row18 col0\" >-0.52%</td>\n",
       "      <td id=\"T_fb75b_row18_col1\" class=\"data row18 col1\" >-0.13%</td>\n",
       "      <td id=\"T_fb75b_row18_col2\" class=\"data row18 col2\" >1.80%</td>\n",
       "      <td id=\"T_fb75b_row18_col3\" class=\"data row18 col3\" >-1.03%</td>\n",
       "      <td id=\"T_fb75b_row18_col4\" class=\"data row18 col4\" >2.30%</td>\n",
       "      <td id=\"T_fb75b_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col10\" class=\"data row18 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col11\" class=\"data row18 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col12\" class=\"data row18 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col13\" class=\"data row18 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row18_col14\" class=\"data row18 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row19\" class=\"row_heading level0 row19\" >111.12</th>\n",
       "      <td id=\"T_fb75b_row19_col0\" class=\"data row19 col0\" >-0.51%</td>\n",
       "      <td id=\"T_fb75b_row19_col1\" class=\"data row19 col1\" >-0.86%</td>\n",
       "      <td id=\"T_fb75b_row19_col2\" class=\"data row19 col2\" >2.28%</td>\n",
       "      <td id=\"T_fb75b_row19_col3\" class=\"data row19 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col4\" class=\"data row19 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col5\" class=\"data row19 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col6\" class=\"data row19 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col7\" class=\"data row19 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col8\" class=\"data row19 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col9\" class=\"data row19 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col10\" class=\"data row19 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col11\" class=\"data row19 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col12\" class=\"data row19 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col13\" class=\"data row19 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row19_col14\" class=\"data row19 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row20\" class=\"row_heading level0 row20\" >155.57</th>\n",
       "      <td id=\"T_fb75b_row20_col0\" class=\"data row20 col0\" >1.19%</td>\n",
       "      <td id=\"T_fb75b_row20_col1\" class=\"data row20 col1\" >-1.33%</td>\n",
       "      <td id=\"T_fb75b_row20_col2\" class=\"data row20 col2\" >1.89%</td>\n",
       "      <td id=\"T_fb75b_row20_col3\" class=\"data row20 col3\" >2.70%</td>\n",
       "      <td id=\"T_fb75b_row20_col4\" class=\"data row20 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col5\" class=\"data row20 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col6\" class=\"data row20 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col7\" class=\"data row20 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col8\" class=\"data row20 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col9\" class=\"data row20 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col10\" class=\"data row20 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col11\" class=\"data row20 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col12\" class=\"data row20 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col13\" class=\"data row20 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row20_col14\" class=\"data row20 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row21\" class=\"row_heading level0 row21\" >217.8</th>\n",
       "      <td id=\"T_fb75b_row21_col0\" class=\"data row21 col0\" >0.12%</td>\n",
       "      <td id=\"T_fb75b_row21_col1\" class=\"data row21 col1\" >-1.82%</td>\n",
       "      <td id=\"T_fb75b_row21_col2\" class=\"data row21 col2\" >0.71%</td>\n",
       "      <td id=\"T_fb75b_row21_col3\" class=\"data row21 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col4\" class=\"data row21 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col5\" class=\"data row21 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col6\" class=\"data row21 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col7\" class=\"data row21 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col8\" class=\"data row21 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col9\" class=\"data row21 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col10\" class=\"data row21 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col11\" class=\"data row21 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col12\" class=\"data row21 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col13\" class=\"data row21 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row21_col14\" class=\"data row21 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row22\" class=\"row_heading level0 row22\" >304.91</th>\n",
       "      <td id=\"T_fb75b_row22_col0\" class=\"data row22 col0\" >-1.33%</td>\n",
       "      <td id=\"T_fb75b_row22_col1\" class=\"data row22 col1\" >-3.37%</td>\n",
       "      <td id=\"T_fb75b_row22_col2\" class=\"data row22 col2\" >-0.47%</td>\n",
       "      <td id=\"T_fb75b_row22_col3\" class=\"data row22 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col4\" class=\"data row22 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col5\" class=\"data row22 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col6\" class=\"data row22 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col7\" class=\"data row22 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col8\" class=\"data row22 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col9\" class=\"data row22 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col10\" class=\"data row22 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col11\" class=\"data row22 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col12\" class=\"data row22 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col13\" class=\"data row22 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row22_col14\" class=\"data row22 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fb75b_level0_row23\" class=\"row_heading level0 row23\" >426.88</th>\n",
       "      <td id=\"T_fb75b_row23_col0\" class=\"data row23 col0\" >-3.53%</td>\n",
       "      <td id=\"T_fb75b_row23_col1\" class=\"data row23 col1\" >-1.34%</td>\n",
       "      <td id=\"T_fb75b_row23_col2\" class=\"data row23 col2\" >-3.72%</td>\n",
       "      <td id=\"T_fb75b_row23_col3\" class=\"data row23 col3\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col4\" class=\"data row23 col4\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col5\" class=\"data row23 col5\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col6\" class=\"data row23 col6\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col7\" class=\"data row23 col7\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col8\" class=\"data row23 col8\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col9\" class=\"data row23 col9\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col10\" class=\"data row23 col10\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col11\" class=\"data row23 col11\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col12\" class=\"data row23 col12\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col13\" class=\"data row23 col13\" ></td>\n",
       "      <td id=\"T_fb75b_row23_col14\" class=\"data row23 col14\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29a211610>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='d_bin'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_W_Metric_raw.groupby(by=['d_bin']).agg('count').reset_index().plot.bar(x='d_bin', y='p', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
